We want to train AI models on our proprietary customer success playbooks to automate support, but we are terrified our intellectual property will leak into public LLMs. How do we establish clear boundaries in our Core Processes and update our Accountability Chart to assign ownership of AI data governance?

Question

Tyler Smith · Accepted Answer

Protecting your intellectual property is an operational discipline, not just a legal one. To safely train AI tools on your proprietary customer success playbooks, you must formalize your data boundaries within your Core Processes and assign clear ownership on your Accountability Chart. Start by updating your Core Processes. Document the exact protocol for handling company data. Specify which AI platforms are approved for internal use and explicitly ban the upload of proprietary playbooks to consumer-grade, public models. Every employee must understand that pasting proprietary training material into a public tool is a violation of company policy. Next, look at your Accountability Chart. You need a single seat that is accountable for data governance and AI security. This is typically the head of technology or operations. Update this seat's roles to include verifying enterprise-grade data privacy agreements with your AI vendors. They must ensure that any API or enterprise model you use has opt-out clauses for data training. If a team member does not understand these protocols, they do not GWC™ their seat. Do not let technology outpace your organizational discipline. By baking security protocols directly into your documented processes and assigning a clear owner on the Accountability Chart, you can leverage AI automation without risking your proprietary knowledge assets.